Replication Data for: Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot

Version 2.0

Yuhas, Michael John, 2022, "Replication Data for: Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot", https://doi.org/10.21979/N9/INOCLV, DR-NTU (Data), V2

Learn about Data Citation Standards.

Contact Owner

Make Data Count (MDC) Metrics

since 2021-11-01

1,413 Views

5 Downloads )

0 Citations

Description	Replication Data for "Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot" This data set contains a series of scenes (sequential images) gathered by a DB21M Duckiebot navigating a straight track inside the HESL (Hardware and Embedded Systems Laboratory). The dataset has already been divided into training, cross-validation, and test sets. For the purpose of out-of-distribution detection, only the test set is labeled. The training and cross-validation sets contain only in-distribution images. (2022-10-20)
Subject	Computer and Information Science
Keyword	Duckietown, Out-of-Distribution Detection
Related Publication	Yuhas, M., & Easwaran, A. (2022). Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot. arXiv preprint arXiv:2211.11520. arXiv: 2211.11520
Funding Information	Ministry of Education (MOE): Tier-2 grant number MOE2019-T2-2-040 National Research Foundation (NRF): Prime Minister’s Office, Singapore under its Campus for Research Excellence National Research Foundation (NRF): Technological Enterprise (CREATE) programme
License/Data Use Agreement	CC BY-NC 4.0

Filter by

	1 to 4 of 4 Files	Download
	CALIBRATE.zip ZIP Archive - 641.5 MB Published Oct 25, 2022 4 Downloads MD5: f7837b3c3fe25b184ab5ed92e7f467ab	Access File File Access Public Download Options ZIP Archive Download Metadata Data File Citation EndNote XML RIS BibTeX
	README.md Markdown Text - 2.2 KB Published Oct 25, 2022 5 Downloads MD5: 9d6c05ef64629d1941bb4671108a2e15 Documentation	Access File File Access Public Download Options Markdown Text Download Metadata Data File Citation EndNote XML RIS BibTeX
	TEST.zip ZIP Archive - 1.6 GB Published Oct 25, 2022 4 Downloads MD5: d7baa2c73e53a45ae7def0b8089f62a0	Access File File Access Public Download Options ZIP Archive Download Metadata Data File Citation EndNote XML RIS BibTeX
	TRAIN.zip ZIP Archive - 1.9 GB Published Oct 25, 2022 4 Downloads MD5: 7f8bb22e027c271ae5aaf1d1884a9d15	Access File File Access Public Download Options ZIP Archive Download Metadata Data File Citation EndNote XML RIS BibTeX

Citation Metadata

Persistent Identifier	doi:10.21979/N9/INOCLV
Publication Date	2022-10-20
Title	Replication Data for: Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot
Author	Yuhas, Michael John (Nanyang Technological University)
Point of Contact	Use email button above to contact. Yuhas Michael John (Nanyang Technological University)
Description	Replication Data for "Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot" This data set contains a series of scenes (sequential images) gathered by a DB21M Duckiebot navigating a straight track inside the HESL (Hardware and Embedded Systems Laboratory). The dataset has already been divided into training, cross-validation, and test sets. For the purpose of out-of-distribution detection, only the test set is labeled. The training and cross-validation sets contain only in-distribution images. (2022-10-20)
Subject	Computer and Information Science
Keyword	Duckietown Out-of-Distribution Detection (OOD Detection)
Related Publication	Yuhas, M., & Easwaran, A. (2022). Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot. arXiv preprint arXiv:2211.11520. arXiv: 2211.11520 https://arxiv.org/abs/2211.11520 Yuhas, M., & Easwaran, A. (2022). Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot. arXiv preprint arXiv:2211.11520. handle: 10356/162460 https://dr.ntu.edu.sg/handle/10356/162460
Notes	Small-scale Dataset for Training an OOD Detector in Duckietown I. Background Duckietown [1] is a low-cost mobile robotics platform used to simulate autonomous vehicles. One important task in autonomous driving is out-of-distribution (OOD) detection [2]. OOD detection involves training a model on only an in-distribution (ID) training set and identifying samples that fall outside of this distribution at test-time. As such, this dataset only contains labels on the test data, all training and calibration data is assumed to be ID. II. Details A. Training The training data was collected from the same track with 4 different backgrounds (areas of the lab). For each background the robot navigated the track three times (runs) and each run became a scene. Each scene is stored in a subdirectory TRAIN/backgroun and contains 640x480 png images. Each sene ranges from 450 to 510 sequential images. B. Calibration The calibration data is gathered from the same 4 backgrounds as the training data, but there is only one scene per background. This data can also be used as cross-validation data as it is assumed to come from the same distribution as the training set. Each scene is stored in a subdirectory CALIBRATE/d. Each scene ranges from 470 to 490 sequential images. C. Test The test set consists of eight scenes that contain some ID images and some OOD images. The OOD images are generated by sprinkling confetti on the track to simulate snowfall. The test images are stored in folders TEST/ood and corresponding CSV files TEST/ood_gt.csv tell whether an image in the folder is ID (0) or OOD (1). III. Contact Dataset author: michaelj004@e.ntu.edu.sg IV. References [1] L. Paull et al., "Duckietown: An open, inexpensive and flexible platform for autonomy education and research," in 2017 IEEE International Conference on Robotics and Automation (ICRA), May 2017, pp. 1497--1504, doi: 10.1109/ICRA.2017.7989179. [2] M. Yuhas, Y. Feng, D. J. X, Ng, Z. Rahiminasab, and A. Easwaran, "Embedded Out-of-Distribution Detection on an Autonomous Robot Platform," in Proceedings of the Workshop on Design Automation for CPS and IoT, May 2021, pp. 13--18, doi:\10.1145/3445034.3460509.
Funding Information	Ministry of Education (MOE): Tier-2 grant number MOE2019-T2-2-040 National Research Foundation (NRF): Prime Minister’s Office, Singapore under its Campus for Research Excellence National Research Foundation (NRF): Technological Enterprise (CREATE) programme
Depositor	Yuhas Michael John
Deposit Date	2022-10-20
Data Type	Image data
Software	ROS, Version: 1

Dataset Terms

License/Data Use Agreement

Our Community Norms as well as good scientific practices expect that proper credit is given via citation. Please use the data citation shown on the dataset page.

CC BY-NC 4.0

Dataset Version	Summary	Contributors	Published on
No records found.

Edit File

This file has already been deleted (or replaced) in the current version. It may not be edited.

Restrict Access

Restricting limits access to published files. People who want to use the restricted files can request access by default. If you disable request access, you must add information about access to the Terms of Access field.

Learn about restricting files and dataset access in the User Guide.

Request Access

Enable access request

You must enable request access or add terms of access to restrict file access.

Terms of Access for Restricted Files

Save Changes

Edit Embargo

The selected file or files have already been published. Contact an administrator to change the embargo date or reason of the file or files.

Delete Files

The file will be deleted after you click on the Delete button.

Files will not be removed from previously published versions of the dataset.

Select File(s)

Please select one or more files.

Share Dataset

Share this dataset on your favorite social media networks.

Dataset Citations

Citations for this dataset are retrieved from Crossref via DataCite using Make Data Count standards. For more information about dataset metrics, please refer to the User Guide.

Sorry, no citations were found.

Restricted Files Selected

The selected file(s) may not be downloaded because you have not been granted access.

Download Options

The files selected are too large to download as a ZIP.

You can select individual files that are below the 953.7 MB download limit from the files table, or use the Data Access API for programmatic access to the files.

Select File(s)

Please select a file or files to be downloaded.

Restricted Files Selected

The restricted file(s) selected may not be downloaded because you have not been granted access.

Click Continue to download the files you have access to download.

Delete Dataset

Are you sure you want to delete this dataset and all of its files? You cannot undelete this dataset.

Delete Draft Version

Are you sure you want to delete this draft version? Files will be reverted to the most recently published version. You cannot undelete this draft.

Unpublished Dataset Private URL

Private URL can only be used with unpublished versions of datasets.

Unpublished Dataset Private URL

Are you sure you want to disable the Private URL? If you have shared the Private URL with others they will no longer be able to use it to access your unpublished dataset.

Delete Files

The file(s) will be deleted after you click on the Delete button.

Files will not be removed from previously published versions of the dataset.

Compute

This dataset contains restricted files you may not compute on because you have not been granted access.

Deaccession Dataset

Are you sure you want to deaccession? The selected version(s) will no longer be viewable by the public.

Deaccession Dataset

Are you sure you want to deaccession this dataset? It will no longer be viewable by the public.

Version Differences Details

Please select two versions to view the differences.

Version Differences Details

Version:
Last Updated:

Select File(s)

Please select a file or files for access request.

Select File(s)

Embargoed files cannot be accessed. Please select an unembargoed file or files for your access request.

Edit Tags

Select existing file tags or create new tags to describe your files. Each file can have more than one tag.

Request Access

You need to Log In to request access.

Dataset Terms

This dataset is made available under the following terms. Please confirm and/or complete the information needed below in order to continue.

License/Data Use Agreement

Our Community Norms as well as good scientific practices expect that proper credit is given via citation. Please use the data citation shown on the dataset page.

CC BY-NC 4.0

Preview Guestbook

Upon downloading files the guestbook asks for the following information.

Guestbook Name

Collected Data

Account Information

Package File Download

Use the Download URL in a Wget command or a download manager to download this package file. Download via web browser is not recommended. User Guide - Downloading a Dataverse Package via URL

Download URL

https://researchdata.ntu.edu.sg/api/access/datafile/

Request Access

Please confirm and/or complete the information needed below in order to request access to files in this dataset.

Compute Batch

Clear Batch

Dataset	Persistent Identifier	Change Compute Batch

Compute Batch

Submit for Review

You will not be able to make changes to this dataset while it is in review.

Publish Dataset

Are you sure you want to republish this dataset?

Publishing dataset means making it publicly available and publicly searchable on the DR-NTU (Data) search engine and third-party search engines (e.g., Google Search or Google Dataset Search).

If you need a second opinion/review, or if you have some concerns, refrain from publishing, and contact NTU Data Librarians (library@ntu.edu.sg).

Please read the following carefully BEFORE you publish your research data:

By posting User Uploads to your dataverse or other dataverses in DR-NTU (Data), or by allowing others to do so, you make the following representations and warranties to DR-NTU (Data):

1. User Uploads do not infringe upon the copyrights or other intellectual property rights, including, but not limited to patent, trademark, trade secret, copyright, right of publicity or other right of any third party;

2. User Uploads do not violate any laws;

3. In the event you become aware of any issues after submitting a User Upload, you will promptly notify DR-NTU (Data) and the relevant DR-NTU (Data) Administrator(s) of any confidentiality, privacy or data protection, licensing, or intellectual property issues regarding the User Uploads;

4. User Uploads do not contain software viruses or any other computer codes, files, or programs that are designed or intended to disrupt, damage, limit or interfere with the proper function of any software, hardware, or telecommunications equipment or to damage or obtain unauthorized access to any system, data files, or other information of DR-NTU (Data) or any third party;

5. User Uploads have been given all relevant, obligatory, and applicable approvals for posting such materials with the content included and in the format uploaded, including but not limited to approvals from the Institutional Review Board and third parties with whom Users have relevant contractual obligations; and

6. User Uploads must be void of all identifiable information, such that re-identification of any subjects from the amalgamation of the information available from all of the materials (across datasets and dataverses) uploaded under any one author and/or User should not be possible. Specifically, User Uploads cannot contain social security numbers; credit card numbers; medical record numbers; health plan numbers; other account numbers of individuals; or biometric identifiers (fingerprints, retina, voice, print, DNA, etc.). The only exceptions for when identifiable information is allowed are when:

a. the information has been previously released to the public;
b. the information describes public figures, where the data relates to their public roles or other non-sensitive subjects; or
c. all identified subjects have given explicit informed consent allowing the public release of the information in the dataset.

Select if this is a minor or major version update.

Minor Release (2.1)

Major Release (3.0)

Publish Dataset

This dataset cannot be published until Safe ML is published by its administrator.

Publish Dataset

This dataset cannot be published until Safe ML and Arvind EASWARAN are published.

Return to Author

Return this dataset to contributor for modification.